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Record W4223932835 · doi:10.1016/j.apmr.2022.03.019

Reporting Guideline for RULER: Rasch Reporting Guideline for Rehabilitation Research: Explanation and Elaboration

2022· letter· en· W4223932835 on OpenAlexaff
Ann Van de Winckel, Allan J. Kozlowski, Mark V. Johnston, Jennifer Weaver, Namrata Grampurohit, Lauren Terhorst, Shannon B. Juengst, Linda Ehrlich‐Jones, Allen W. Heinemann, John L. Melvin, Pallavi Sood, Trudy Mallinson

Bibliographic record

VenueArchives of Physical Medicine and Rehabilitation · 2022
Typeletter
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsInstitute of Aging
FundersU.S. Department of Defense
KeywordsChecklistRasch modelGuidelineTransparency (behavior)RehabilitationConsistency (knowledge bases)Consolidated Standards of Reporting TrialsProtocol (science)RulerContext (archaeology)PsychologyMedicineApplied psychologyMedical educationPhysical therapyNursingComputer scienceAlternative medicinePsychological intervention

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.200
metaresearch head score (Gemma)0.655
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2000.655
Meta-epidemiology (narrow)0.0030.008
Meta-epidemiology (broad)0.0120.013
Bibliometrics0.0140.013
Science and technology studies0.0050.006
Scholarly communication0.0150.008
Open science0.0100.007
Research integrity0.0340.032
Insufficient payload (model declined to judge)0.0330.055

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.501
GPT teacher head0.562
Teacher spread0.062 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations68
Published2022
Admission routes1
Has abstractno

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